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Iterative learning of impedance control

机译:阻抗控制的迭代学习

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摘要

This paper proposes an iterative learning control scheme for impedance control of robotic tasks when the tool endpoint covered by soft and deformable material presses a rigid object or environment at a prescribed periodic force pattern. To this end, an iterative learning control scheme for a class of linear dynamical systems with a negative feedback structure is analyzed and convergence of the proposed learning update law after a sufficient number of repetitions is proved. It is shown that this convergence realizes impedance matching in a sense of electric circuit theory of the feedback system can be expressed as a lumped-parameter electric circuit. The iterative learning control scheme is then applied for a case of impedance control of robotic tasks when the characteristics of reproducing force of the deformable material is nonlinear in its displacement and unknown and the tool mass is uncertain. Simulation results are also presented, which show effectiveness of the proposed learning control scheme.
机译:本文提出了一种迭代学习控制方案,该方案用于在由柔软且可变形材料覆盖的工具端点以指定的周期性力模式挤压刚性物体或环境时,对机器人任务进行阻抗控制。为此,分析了一类具有负反馈结构的线性动力学系统的迭代学习控制方案,并证明了经过足够的重复次数后,所提出的学习更新定律具有收敛性。从反馈系统的电路理论的意义上说,这种收敛实现了阻抗匹配,可以表示为集总参数电路。然后,当可变形材料的再生力的位移特性为非线性且未知且工具质量不确定时,将迭代学习控制方案应用于机器人任务的阻抗控制情况。仿真结果也被提出,表明所提出的学习控制方案的有效性。

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